Present research work on the Internet of Things and Fog computing has taken hype due to the low cost of building them by optimized sensors. Wireless sensor devices play a vital role in developing IoT sensing infrastructure. These devices are connected to form a network known as the WSN assisted IoT network. The number of things is verdant in IoT creates an immense energy need. So, efficient energy utilization is required to alter the green IoT environment. These sensor devices are having limited power and computational capabilities. We can use energy-efficient data routing protocols as data transferring with these low powered sensors is very challenging. One may use the sensors for more time in sensing the environment and sending the information. This proposed protocol deals with a reliable routing protocol for IoT sensing Infrastructure. Initially, a rendezvous region was created in the middle of the network area. Clustering and multipath technique are used because it reduces energy consumption and increases reliability. The proposed protocol is simulated within Castalia simulator for attaining the performance beneath different characteristics like packet delivery ratio, the average energy consumption, end to end delay, and network lifetime. It is observed that the suggested technique is useful in energy consumption and helps in boosting the IoT infrastructure network lifetime.

Fruit recognition with its variety classification is a promising area of research. This research is useful for monitoring and categorizing the fruits according to their kind with the assurance of fast production chain. In this research, we establish a new high-quality dataset of images containing the five most popular oval-shaped fruits with their varieties. Recent work in deep neural networks has led to the development of many new applications related to precision agriculture, including fruit recognition. This paper proposes a classification model for 40 kinds of Indian fruits by support vector machine (SVM) classifier using deep features extracted from the fully connected layer of the convolutional neural network (CNN) model. Also, another approach based on transfer learning is proposed for recognition of Indian Fruits. The experiments are carried out in six most powerful deep learning architectures such as AlexNet, GoogleNet, ResNet-50, ResNet-18, VGGNet-16 and VGGNet-19. So, the six deep learning architectures are evaluated in two approaches, which makes 12 classification model in total. The performance of each classification model is assessed in terms of accuracy, sensitivity, specificity, precision, false positive rate (FPR), F1 score, Mathew correlation (MCC) and Kappa. The evaluation results show that the SVM classifier using deep learning feature provides better results than their transfer learning counterparts. The deep learning feature of VGG16 and SVM results in 100% in terms of accuracy, sensitivity, specificity, precision, F1 score and MCC at its highest level.

In this digitalised world where every information is stored, the data a are growing exponentially. It is estimated that data are doubles itself every two years. Geospatial data are one of the prime contributors to the big data scenario. There are numerous tools of the big data analytics. But not all the big data analytics tools are capabilities to handle geospatial big data. In the present paper, it has been discussed about the recent two popular open source geospatial big data analytical tools i.e. SpatialHadoop and GeoSpark which can be used for analysis and process the geospatial big data in efficient manner. It has compared the architectural view of SpatialHadoop and GeoSpark. Through the architectural comparison, it has also summarised the merits and demerits of these tools according the execution times and volume of the data which has been used.